Takeshi Nikawa | Biochemistry | Research Excellence Award

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Prof. Dr. Takeshi Nikawa | Biochemistry | Research Excellence Award

Tokushima University Graduate School | Japan

Prof. Dr. Takeshi Nikawa is a distinguished researcher at Tokushima University, Japan, with expertise in skeletal muscle physiology, molecular biology, and nutritional interventions. His research explores the mechanisms underlying muscle atrophy, mitochondrial function, and gene regulation during myogenesis, aiming to understand how these processes impact aging, metabolism, and overall health. Nikawa’s work integrates experimental studies with translational approaches to develop strategies for maintaining muscle mass and function, particularly in aging populations or individuals at risk of muscle degeneration. He actively collaborates with international scientists across multiple disciplines, fostering knowledge exchange and advancing global research initiatives. Through his publications and applied studies, Nikawa contributes to both fundamental scientific understanding and practical interventions, supporting the development of therapeutic, nutritional, and lifestyle strategies that enhance quality of life and address key societal challenges related to health and aging.

Citation Metrics (Scopus)

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Featured Publications

Lili Zhan | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Lili Zhan | Artificial Intelligence | Best Researcher Award

Associate Professor| Shandong University of Science and Technology | China

Assoc. Prof. Dr. Lili Zhan is a researcher whose work spans remote sensing, Arctic cryosphere monitoring, computer vision, and artificial intelligence–enhanced educational systems. Her scholarship incorporates both physical environmental analysis and advanced data-driven methodologies, with representative contributions including sensitivity analyses of microwave brightness temperature to variations in snow depth on Arctic sea ice, a deep-learning-based remote-sensing scene-classification framework employing EfficientNet-B7, and an improved YOLOv7 instance-segmentation method for ship detection in complex SAR imagery Lili-Zhan. She has also contributed to the design and implementation of intelligent teaching models grounded in contemporary AI and data-centric approaches, demonstrating interdisciplinarity across geospatial sciences and educational technology Lili-Zhan Across these domains, her work reflects a sustained commitment to methodological innovation, integrating state-of-the-art neural architectures with domain-specific challenges in environmental monitoring and maritime situational awareness. Her collaborations often bridge academic research groups focused on cryosphere change, Earth observation, and applied machine learning, enabling the development of tools that support improved climate understanding, maritime safety, and digital-education modernization. Although publication and citation metrics are not specified in the available document, the range of research topics and representative studies indicates a growing scholarly profile with contributions positioned at the intersection of remote-sensing physics and intelligent systems engineering. Collectively, her work holds global societal relevance: enhancing the accuracy of cryospheric measurements supports climate-model improvement and polar-region policy planning; advancing ship-detection techniques contributes to marine governance, environmental protection, and emergency response; and promoting AI-supported pedagogical frameworks aids the digital transformation of education.

Profile: Scopus 

Featured Publications

Zhan, L. (Year). SAR ship target instance segmentation based on SISS-YOLO. Journal Name, Volume(Issue), pages.

Lili Zhan’s work advances the precision of remote-sensing analytics and intelligent detection systems, strengthening global capabilities in environmental monitoring and maritime safety. Her innovations support science-driven decision-making with direct benefits for climate resilience and societal securit

Mona Almutairi | Artificial Intelligence | Best Researcher Award

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Ms. Mona Almutairi | Artificial Intelligence | Best Researcher Award

Shaqra University | Saudi Arabia

Ms. Mona Almutairi is a highly motivated computer science graduate with a strong academic foundation and practical experience in system engineering and data management. She completed her Bachelor’s degree in Computer Science from Shaqra University in 2019 with an impressive GPA of 4.19 out of 5, demonstrating consistent academic excellence. Her professional experience includes serving as a System Engineer at the Ministry of Economy and Planning, where she contributed to optimizing systems operations and enhancing digital workflows, as well as volunteering as a Data Entry Assistant at the Ministry of Health, where she efficiently managed and organized large datasets with accuracy and confidentiality. She further enriched her technical expertise through professional courses in Software Engineering from the Saudi Digital Academy and Web Development from the Ministry of Communications and Information Technology, equipping her with up-to-date industry knowledge and coding proficiency. Her research interests lie in software development, data analysis, and emerging technologies that integrate innovation with societal advancement. Ms. Almutairi’s research skills include proficiency in data analysis tools, problem-solving, and the ability to apply algorithmic thinking to real-world challenges. She is also adept at using Microsoft Office and has strong communication, teamwork, and adaptability skills, making her a collaborative and reliable professional. Her dedication to learning and excellence has been recognized through various academic and professional achievements, reflecting her commitment to continuous improvement. Overall, Ms. Almutairi is a forward-thinking computer scientist who combines technical knowledge, analytical capabilities, and professional experience to drive innovation in the field of information technology.

Profiles: Google Scholar | ORCID

Featured Publications

Almutairi, M., & Dardouri, S. (2025). Intelligent hybrid modeling for heart disease prediction. Information, 16(10), 869. Citations: 1

Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

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Mr. Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

Biomedical Engineering at Pukyong National University, South Korea

Nhan T. Nguyen, a Master’s student at Pukyong National University, is a promising early-career researcher specializing in biomedical engineering, computer vision, and artificial intelligence. His research focuses on non-destructive testing, low-level vision, and automated inspection systems using advanced AI techniques such as GANs, transformers, and diffusion models. Nhan has contributed to multiple peer-reviewed publications in prestigious journals like IEEE Transactions and MDPI Applied Sciences, with additional manuscripts under review and in preparation. His work demonstrates strong practical relevance, with AI models deployed in industrial applications including semiconductor inspection, robotic automation, and smart city infrastructure. He has received several academic honors and awards, reflecting his dedication and innovation. Despite being at the master’s level, he serves as a peer reviewer for international journals and conferences, highlighting his scholarly maturity. With interdisciplinary expertise, a growing publication record, and impactful real-world applications, Nhan is highly suitable for the Best Researcher Award in the early-career category.

Professional Profile 

Education🎓

Nhan T. Nguyen has built a strong educational foundation in engineering and artificial intelligence across reputable institutions in Vietnam and South Korea. He earned his Bachelor of Science degree in Information Technology Engineering from Ho Chi Minh University of Technology, where he was actively involved in undergraduate research and received multiple academic awards and scholarships. During his undergraduate years, he developed projects integrating AI with OCR and chatbot systems. Currently, he is pursuing a Master’s degree in the Industry 4.0 Convergence Bionics Engineering program at Pukyong National University in South Korea under the supervision of Professor Junghwan Oh. His graduate research focuses on non-destructive testing, specifically in scanning acoustic microscopy systems, and applying AI to industrial inspection tasks. Through this academic journey, Nhan has gained in-depth knowledge and hands-on experience in computer vision, machine learning, and robotics, forming a strong educational background that supports his innovative contributions to research and industry applications.

Professional Experience📝

Nhan T. Nguyen has gained diverse professional experience in the fields of artificial intelligence, computer vision, and industrial automation. He served as an AI Engineer at the Artificial Intelligence Center of FPT Software in Vietnam, where he worked on optimizing dehumidification processes for the Chicago Art Museum and enhancing defect detection in steel production using machine learning algorithms. His role involved data analysis, predictive modeling, and AI deployment in real-world environments. He also contributed to a deep learning-based search engine enhancement project for a pharmaceutical retail company. In addition, at FPT Information System’s Smart City Department, he developed camera-based systems for sidewalk encroachment detection, which were integrated into Ho Chi Minh City’s traffic management system. Currently, as a Graduate Research Assistant at Pukyong National University, he is involved in automating weld inspection systems and developing AI models for defect detection in scanning acoustic microscopy. His experience bridges academic research and practical industrial implementation.

Research Interest🔎

Nhan T. Nguyen’s research interests lie at the intersection of artificial intelligence, computer vision, and industrial automation, with a particular focus on low-level vision tasks and non-destructive testing. He is passionate about developing advanced AI models such as Generative Adversarial Networks (GANs), transformers, and diffusion models for applications in image restoration, super-resolution, and defect detection. His work emphasizes enhancing the performance and reliability of automated inspection systems used in semiconductor manufacturing, steel production, and other industrial settings. Nhan is also interested in integrating AI with robotic systems, using tools like 3D scanners, lasers, and cameras to automate surface inspection processes. Additionally, he explores exploratory data analysis across multiple domains, including medical, environmental, and industrial datasets. His goal is to bridge the gap between theoretical research and practical implementation, contributing to more intelligent, accurate, and efficient inspection and monitoring systems in smart manufacturing and biomedical engineering environments.

Award and Honor🏆

Nhan T. Nguyen has received numerous awards and honors in recognition of his academic excellence, innovative research, and technical achievements. He was awarded a scholarship by Pukyong National University in 2023 for his outstanding performance as a graduate student. During his undergraduate studies at Ho Chi Minh University of Technology, he received the prestigious KMS Technology Scholarship in 2022, as well as the City Now Company Scholarship and the Impressive Award in the HUTECT Start-up Wing competition in 2021. He also earned a Consolation Prize in the university’s AI Hackathon in 2020 and was recognized for his undergraduate research contributions. Nhan consistently demonstrated academic excellence, earning the Outstanding Undergraduate Student Scholarship in 2018. These honors reflect his dedication to research, creativity in problem-solving, and strong commitment to applying AI technologies to real-world challenges. His consistent recognition throughout his academic career underscores his potential as a leading researcher in his field.

Research Skill🔬

Nhan T. Nguyen possesses a robust set of research skills that span artificial intelligence, computer vision, and industrial automation. He is highly proficient in data processing, exploratory data analysis, and model development using Python and advanced machine learning frameworks. His expertise includes designing and implementing deep learning models, particularly using Generative Adversarial Networks (GANs), transformers, and diffusion models for image super-resolution, denoising, and defect detection. Nhan is skilled in integrating AI models with hardware systems such as robotic arms, 3D scanners, lasers, and industrial cameras to build intelligent inspection systems. He has hands-on experience with non-destructive testing methods, particularly scanning acoustic microscopy, and is adept at handling real-world industrial datasets. Additionally, Nhan is capable of deploying AI solutions into operational environments, enhancing automation processes in sectors like semiconductor manufacturing, smart cities, and healthcare. His ability to bridge theoretical models with practical applications showcases his strong technical and problem-solving capabilities as a researcher.

Conclusion💡

Nhan T. Nguyen demonstrates exceptional promise and proven capability in applied AI and biomedical inspection research, with practical impact, strong publications, and academic service. For a master’s-level researcher, this profile is outstanding.

Publications Top Noted✍️

📄 1. GAN-Based Super-Resolution in Linear R-SAM Imaging for Enhanced Non-Destructive Semiconductor Measurement

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Le Hai Tran, Vu Hoang Minh Doan, Van Bang Nguyen, Wonjo Lee, Sudip Mondal, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.3390/app15126780

  • Source: Applied Sciences, Published on June 17, 2025

📄 2. Transformer-Based Super-Resolution Scanning Acoustic Imaging for Industrial Inspection

  • Authors: Trong Nhan Nguyen, Vu Hoang Minh Doan, Tan Hung Vo, Jaeyeop Choi, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.1109/icit63637.2025.10965207

  • Source: 2025 IEEE International Conference on Industrial Technology (ICIT), Published on March 26, 2025

📄 3. Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Sudip Mondal, Junghwan Oh

  • Year: 2024

  • Citation (DOI): 10.1109/TSM.2024.3444850

  • Source: IEEE Transactions on Semiconductor Manufacturing, Published in November 2024

Nalini Manogara | Artificial Intelligence | Best Academic Researcher Award

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Dr. Nalini Manogara | Artificial Intelligence |  Best Academic Researcher Award

Associate Professor  at S.A. Engineering College, India

Dr. M. Nalini is a distinguished academician with over 14 years of teaching and research experience in Computer Science and Engineering. Currently serving as an Associate Professor, she has demonstrated excellence in academia through her impactful publications in high-ranking SCI and Scopus-indexed journals, focusing on areas like wireless sensor networks, cloud healthcare systems, and network security. Dr. Nalini has received several prestigious awards, including the Best Research Award (2019) and Academic Excellence Award (2024). She has actively contributed to academic leadership by organizing symposiums, FDPs, and conferences, while also mentoring Ph.D. scholars and engineering students. A recipient of multiple IEEE-sponsored grants, she is an active member of several professional bodies such as IEEE, ISTE, and ACM. Her commitment to academic growth, curriculum development, and research funding showcases her dedication to advancing education and technology. Dr. Nalini is a highly deserving candidate for the Best Academic Researcher Award.

Professional Profile 

Education🎓

Dr. M. Nalini has a strong academic foundation in Computer Science and Engineering, marked by consistent academic excellence throughout her educational journey. She earned her Ph.D. in Computer Science and Engineering from St. Peter’s Institute of Higher Education and Research in 2018, where she conducted research on efficient anomaly detection and data redundancy elimination. Prior to that, she completed her M.Tech in Computer Science and Engineering from B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, in 2012 with an impressive CGPA of 9.1, securing the University’s third rank. Her undergraduate studies were completed at V.P.M.M. College for Women, affiliated with Anna University, where she received a B.E. in Computer Science and Engineering in 2010. She also demonstrated academic excellence in her school years, securing 91% in SSLC and 73.42% in HSC. In 2024, she further enriched her academic credentials by completing a Post-Doctoral Fellowship, expanding her research expertise.

Professional Experience📝

Dr. M. Nalini brings over 14 years of diverse professional experience in academia and industry, showcasing a progressive career in teaching, research, and leadership. She began her academic journey as a Lecturer at Sakthi Mariamman Engineering College (2010–2012), followed by roles as Assistant Professor at RVS Padhmavathy College and Sri Nandhanam College of Engineering and Technology, where she contributed to academic excellence and student mentoring. In 2018, she gained valuable industry exposure as a Software Trainee at J.J. Automation Pvt. Ltd., enriching her practical understanding of technology. She then served as Assistant Professor at Saveetha School of Engineering until mid-2022, where she was actively involved in research and faculty development programs. Currently, she is an Associate Professor at S.A. Engineering College, where she leads academic initiatives, mentors Ph.D. scholars, and coordinates national and international academic events. Her well-rounded experience highlights her dedication to both academic advancement and professional excellence.

Research Interest🔎

Dr. M. Nalini’s research interests lie at the intersection of advanced computing technologies and real-world applications, with a strong focus on data mining, machine learning, wireless sensor networks, and network security. Her scholarly work explores intelligent systems capable of detecting anomalies, optimizing data storage, and enhancing communication protocols, particularly in the context of large-scale data environments. She has conducted extensive research on intrusion detection systems, cloud-based healthcare applications, and AI-driven behavioral prediction models, contributing significantly to the fields of cybersecurity and smart computing. Dr. Nalini is also deeply interested in emerging areas such as explainable artificial intelligence (XAI), Internet of Things (IoT), and edge computing. Her projects emphasize both theoretical frameworks and practical implementation, aimed at developing scalable and efficient solutions for complex problems. Through her research, she aims to bridge the gap between academic innovation and industrial application, fostering technological advancement and societal impact.

Award and Honor🏆

Dr. M. Nalini has been widely recognized for her academic excellence and impactful contributions to research and education. She received the prestigious Best Research Award in 2019 from the International Association for Science and Technical Education (IASTE), acknowledging her innovative work in computer science. In 2020, she was honored with the Best Women Faculty Award by the Amaravathi Research Academy’s Faculty Excellence Awards, highlighting her dedication to teaching and mentoring. Most recently, she earned the Academic Excellence Award in 2024 from the Association of Intellectual Professionals (AIP), a testament to her consistent academic performance and leadership in scholarly activities. In addition, she has served as a resource person in ATAL Faculty Development Programs, completed multiple certifications including NPTEL courses, and has received significant funding and sponsorships for technical events and faculty development initiatives from reputed bodies such as IEEE, ACM, and CSI. These accolades reflect her outstanding professional achievements and leadership in academia.

Research Skill🔬

Dr. M. Nalini possesses a robust set of research skills that reflect her deep expertise in computer science and engineering. Her proficiency spans key domains such as data mining, machine learning, artificial intelligence, cloud computing, and network security. She is skilled in developing innovative algorithms for intrusion detection, anomaly detection, and data deduplication, with proven results published in SCI and Scopus-indexed journals. Dr. Nalini is adept at using various programming languages including C, C++, Java, and tools like XML, HTML, and PHP for web-based applications. Her ability to conduct high-quality empirical research, design complex experimental setups, and apply optimization models to real-world challenges demonstrates her analytical depth. She is also experienced in guiding Ph.D., M.Tech, and B.E. students in research projects, helping them translate ideas into tangible outcomes. With strong writing, critical thinking, and technical documentation skills, Dr. Nalini effectively communicates her findings to both academic and professional communities.

Conclusion💡

Dr. M. Nalini possesses the scholarly depth, leadership, technical expertise, and academic service credentials to deserve strong consideration for the Best Academic Researcher Award. Her consistent record of research, publication in reputed journals, mentoring roles, academic event leadership, and recognized contributions to the academic community affirm her excellence in academia.

Publications Top Noted✍️

  1. An efficient cloud‐based healthcare services paradigm for chronic kidney disease prediction application using boosted support vector machine

    • Authors: J. Aswini, B. Yamini, R. Jatothu, K.S. Nayaki, M. Nalini

    • Year: 2022

    • Citations: 57

  2. Characterization of Rubia cordifolia L. root extract and its evaluation of cardioprotective effect in Wistar rat model

    • Authors: B.S. Chandrashekar, S. Prabhakara, T. Mohan, D. Shabeer, B. Bhandare, et al.

    • Year: 2018

    • Citations: 56

  3. Energy-efficient cluster-based routing protocol for WSN based on hybrid BSO–TLBO optimization model

    • Authors: K. Krishnan, B. Yamini, W.M. Alenazy, M. Nalini

    • Year: 2021

    • Citations: 51

  4. A comprehensive survey on Naive Bayes algorithm: Advantages, limitations and applications

    • Authors: P.J.B. Pajila, B.G. Sheena, A. Gayathri, J. Aswini, M. Nalini

    • Year: 2023

    • Citations: 26

  5. Opportunities for improving crop water productivity through genetic enhancement of dryland crops

    • Authors: C.L.L. Gowda, R. Serraj, G. Srinivasan, Y.S. Chauhan, B.V.S. Reddy, K.N. Rai, et al.

    • Year: 2009

    • Citations: 25

  6. Predictive modelling for lung cancer detection using machine learning techniques

    • Authors: B. Yamini, K. Sudha, M. Nalini, G. Kavitha, R.S. Subramanian, R. Sugumar

    • Year: 2023

    • Citations: 22

  7. AI and IoT applications in medical domain enhancing healthcare through technology integration

    • Authors: K. Sudha, C. Ambhika, B. Maheswari, P. Girija, M. Nalini

    • Year: 2023

    • Citations: 19

  8. Energy harvesting and management from ambient RF radiation

    • Authors: M. Nalini, J.V.N. Kumar, R.M. Kumar, M. Vignesh

    • Year: 2017

    • Citations: 18

  9. Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

    • Authors: G.M. Kumar, M. Nalini

    • Year: 2021

    • Citations: 11

  10. Anomaly Detection Via Eliminating Data Redundancy and Rectifying Data Error in Uncertain Data Streams

  • Authors: S.A. M. Nalini

  • Year: 2014

  • Citations: 11

Afeez Soladoye | Machine learning | Young Scientist Award

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Mr. AfeezSoladoye | Machine learning | Young Scientist Award

Lecturer at Federal university Oye-Ekiti, Nigeria

Soladoye Afeez Adekunle is a promising young scholar in Computer Engineering, currently pursuing his Ph.D. at the Federal University Oye-Ekiti. With a Master’s degree earned with distinction, he has demonstrated strong academic and research capabilities. His work spans machine learning, artificial intelligence, and applied computing, including the development of medical prediction systems and fake news detection using deep learning. In addition to his teaching responsibilities at undergraduate and postgraduate levels, he actively contributes as a peer reviewer for reputable journals such as BMJ Open and serves as a technical editor. His involvement in academic committees and university-level projects reflects his leadership and dedication to institutional development. While his practical projects are impactful, the inclusion of more peer-reviewed publications and measurable research outcomes would further enhance his profile. Overall, his commitment to innovation, education, and research makes him a suitable and competitive candidate for the Young Scientist Award.

Professional Profile

Education🎓

Soladoye Afeez Adekunle has a solid educational background in Computer Engineering, reflecting his dedication to academic excellence and continuous professional development. He is currently pursuing a Ph.D. in Computer Engineering at the Federal University Oye-Ekiti, Nigeria, with a research focus on advanced computing and intelligent systems. He previously earned a Master of Engineering (M.Eng) in Computer Engineering from the same university, graduating with distinction in 2023. His undergraduate studies were completed at Ladoke Akintola University of Technology, Ogbomosho, where he obtained a Bachelor of Technology (B.Tech) degree in Computer Engineering in 2016. His foundational education includes a Senior School Leaving Certificate from Foundation Model College, Ikirun, in 2009, and a Primary School Leaving Certificate from Al-hilal Nursery and Primary School, Ikirun, in 2003. His academic journey reflects a consistent commitment to learning, skill acquisition, and growth in the field of computer science and engineering, preparing him for a successful career in research and education.

Professional Experience📝

Soladoye Afeez Adekunle has amassed valuable professional experience across academia, research, and industry. He currently serves as a Lecturer II in the Department of Computer Engineering at the Federal University Oye-Ekiti, where he teaches both undergraduate and postgraduate courses, supervises student projects, and mentors young researchers. In addition to his teaching role, he is the Assistant Examination Officer and Level Advisor, playing a vital role in exam coordination and academic advising. He also contributes as a Technical Editor for the FUOYE Journal of Engineering and Technology and reviews scholarly articles for esteemed journals like BMJ Open and the Nigerian Journal of Technological Development. As a freelance Machine Learning Engineer, he has developed predictive systems for medical diagnosis and fake news detection, showcasing his ability to apply research in practical contexts. His previous roles include network engineering trainee and peer tutor, reflecting a versatile and well-rounded professional path in computer science and engineering.

Research Interest🔎

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Award and Honor🏆

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Research Skill🔬

Soladoye Afeez Adekunle possesses a diverse and practical set of research skills that align with cutting-edge developments in computer engineering and artificial intelligence. His expertise includes data analysis, machine learning model development, deep learning, and natural language processing. He has applied these skills in various impactful projects such as medical prediction systems for cancer and stroke, fake news detection, and object measurement using computer vision techniques. Adept at data preprocessing, model training, performance evaluation, and algorithm optimization, he ensures high-quality and accurate research outcomes. He is also skilled in using tools and frameworks such as Python, TensorFlow, Keras, and MATLAB for simulation and modeling. His experience in peer reviewing academic journals and formatting manuscripts further demonstrates his understanding of scientific writing and research ethics. Soladoye’s ability to merge academic research with practical application, along with his commitment to innovation, positions him as a capable and forward-thinking researcher in the technology domain.

Conclusion💡

Soladoye, Afeez Adekunle presents a strong case for the Young Scientist Award, especially in the areas of emerging technologies, machine learning, and applied computing. His academic excellence, teaching versatility, peer-review contributions, and practical ML project development demonstrate his passion and potential.

Publications Top Noted✍️

  • Title: IMPACT OF SOCIAL MEDIA ON POLICE BRUTALITY AWARENESS IN NIGERIA

    • Authors: OJOA, SOLADOYE Afeez A.

    • Year: 2020

    • Citations: 24

  • Title: Detection of Cervical Cancer Using Deep Transfer Learning

    • Authors: B.A. Omodunbi, A.A. Soladoye, A.O. Esan, N.S. Okomba, T.G.O.O.M. Ojelabi

    • Year: 2024

    • Citations: 4*

  • Title: Optimizing Stroke Prediction Using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

    • Authors: A.A. Soladoye, D.B. Olawade, I.A. Adeyanju, O.M. Akpa, N. Aderinto, et al.

    • Year: 2025

    • Citations: 2

  • Title: E-learning: Significance on Federal Unity Schools Students’ in Nigeria Amidst COVID-19 Lockdown

    • Authors: A.A. Soladoye

    • Year: 2020

    • Citations: 2

  • Title: Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

    • Authors: B.A. Omodunbi, A.A. Soladoye, N.S. Okomba, M.O. Ayinla, C.S. Odeyemi

    • Year: [Year not specified]

    • Citations: 2*

  • Title: Smart Hospitality: Leveraging Technological Advances to Enhance Customer Satisfaction

    • Authors: O.O. Osadare, O.N. Akande, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Internet of Things (IoT) Based Remote Surveillance Camera for Supervision of Examinations

    • Authors: C. Segun Odeyemi, B.A. Omodunbi, O.M. Olaniyan, A.A. Soladoye

    • Year: 2024

    • Citations: 1

  • Title: Prediction of Customer Satisfaction in Airline Hospitality Services for Improved Service Delivery Using Support Vector Machine

    • Authors: A.A. Sobowale, O.O. Osadare, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Development of an Interactive Android-Based Ayo-Olopon Game

    • Authors: E.Y. Bolaji Abigail Omodunbi, Afeez Adekunle Soladoye, Opeyemi Asaolu

    • Year: 2023

    • Citations: 1

Xiang Li | Computer Science | Best Researcher Award

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Ms. Xiang Li | Computer Science | Best Researcher Award

PHD candidate at University of Chinese Academy of Sciences, China

Xiang Li, a Ph.D. candidate at the University of Chinese Academy of Sciences, demonstrates exceptional potential for the Best Researcher Award. With a solid academic foundation—ranking in the top 5–7% throughout his studies—he has excelled in areas such as deep learning, stochastic processes, and pattern recognition. His research focuses on cross-domain few-shot learning, addressing real-world challenges like medical lesion detection and remote sensing scene classification. He has published in the prestigious Knowledge-Based Systems journal and submitted another to IEEE Transactions on Geoscience and Remote Sensing. Xiang has also earned accolades, including the Second Prize in the National Mathematical Modeling Competition and a top-tier finish in the Huawei Software Elite Challenge. His future interests in class-incremental learning and prompt tuning highlight a clear vision for impactful research. Overall, Xiang Li’s innovative contributions, academic excellence, and commitment to advancing AI technologies make him a strong and deserving candidate for this recognition.

Professional Profile 

Education

Xiang Li has demonstrated outstanding academic performance throughout his educational journey. He earned his Bachelor’s degree in Information and Computer Science from Shandong University, graduating in July 2021 with an impressive GPA of 91.73/100, placing him in the top 7.46% of his class. His coursework included high-level subjects such as Mathematical Statistics, Operations Research, and Advanced Algebra, in which he consistently achieved top scores. Following this, he was admitted to the University of Chinese Academy of Sciences, where he completed foundational Ph.D. training from September 2021 to July 2022, ranking in the top 5% with a GPA of 87.13/100. His advanced studies covered critical areas like Matrix Analysis, Deep Learning, and Pattern Recognition. Currently, he is conducting doctoral research at the Institute of Optics and Electronics, Chinese Academy of Sciences, focusing on cross-domain few-shot learning. His educational background reflects strong technical competence and a solid foundation for innovative research.

Professional Experience

Xiang Li has accumulated valuable professional research experience during his Ph.D. studies at the Institute of Optics and Electronics, Chinese Academy of Sciences. His primary research focuses on cross-domain few-shot learning, a vital area in artificial intelligence that addresses challenges in data-scarce environments. He has led and contributed to key projects, including the development of a dynamic representation enhancement framework to improve model generalization across different domains, and the fine-tuning of general pre-trained models for few-shot remote sensing scene classification. In addition to research, Xiang has actively participated in national competitions, winning third prize in the Huawei Software Elite Challenge for designing a traffic scheduling plan and contributing to infrared small target detection strategies in another competition. These experiences highlight his strong technical problem-solving skills, teamwork, and ability to apply theoretical knowledge to real-world challenges. His professional work reflects both depth and versatility, positioning him as a highly capable and innovative young researcher.

Research Interest

Xiang Li’s research interests lie at the forefront of artificial intelligence, with a strong focus on cross-domain few-shot learning, computer vision, and representation learning. He is particularly interested in developing algorithms that enable models to perform effectively in data-scarce scenarios, addressing the challenges posed by domain shifts and limited labeled data. His current work involves enhancing the representational capacity of models to learn diverse and meaningful features across domains, with applications in medical image analysis and remote sensing. Xiang is also exploring techniques for fine-tuning general pre-trained models to adapt to new tasks without extensive retraining. Looking ahead, he is keen on advancing research in few-shot class-incremental learning, where models continuously adapt to new classes with minimal data, and in prompt tuning for vision-language pre-trained models, which integrates natural language processing with visual recognition. His interests reflect a forward-thinking approach to building intelligent systems capable of learning efficiently and generalizing across tasks.

Award and Honor

Xiang Li has received several prestigious awards and honors in recognition of his academic excellence and research capabilities. During his undergraduate and doctoral studies, he was consistently awarded scholarships from both Shandong University and the University of Chinese Academy of Sciences, reflecting his outstanding academic performance and dedication. In June 2022, he was named a Merit Student at the University of Chinese Academy of Sciences, an honor reserved for top-performing students. His strong analytical and problem-solving skills were further recognized in national competitions, where he earned the Second Prize in the National College Students’ Mathematical Modeling Competition in 2019. Additionally, he played a key role in a team that won third prize in the Huawei Software Elite Challenge, a highly competitive event involving over 300 teams. These honors highlight his ability to excel both academically and practically, reinforcing his position as a promising and accomplished young researcher in the field of computer science.

Research skill

Xiang Li possesses a strong set of research skills that make him a capable and innovative scholar in the field of artificial intelligence and computer vision. His expertise spans advanced areas such as cross-domain few-shot learning, deep learning, and representation learning. He demonstrates exceptional analytical abilities, evident in his design and implementation of dynamic representation frameworks to enhance model generalization across diverse domains. Xiang is proficient in applying theoretical concepts to practical problems, as seen in his work on fine-tuning pre-trained models for remote sensing scene classification. His skill set includes programming, algorithm development, statistical analysis, and critical thinking, which he has effectively applied in both solo research and collaborative projects. Furthermore, his ability to publish in top-tier journals, such as Knowledge-Based Systems, reflects his competence in scientific writing, experimental design, and result interpretation. These research skills enable him to tackle complex challenges and contribute meaningfully to the advancement of intelligent systems.

Conclusion

Xiang Li is a highly promising young researcher with a solid academic foundation, well-defined research focus, and impactful contributions in the field of computer vision and machine learning. His achievements in cross-domain few-shot learning, publication in a top-tier journal, and award-winning competition experience clearly demonstrate excellence in research and innovation.

Publications Top Noted

  • Title: RSGPT: A remote sensing vision language model and benchmark
    Authors: Y. Hu, Yuan; J. Yuan, Jianlong; C. Wen, Congcong; Y. Liu, Yu; X. Li, Xiang
    Year: 2025

  • Title: Uni3DL: A Unified Model for 3D Vision-Language Understanding
    Authors: X. Li, Xiang; J. Ding, Jian; Z. Chen, Zhaoyang; M. Elhoseiny, Mohamed
    Year: 2025 (Conference Paper)

  • Title: 3D Shape Contrastive Representation Learning With Adversarial Examples
    Authors: C. Wen, Congcong; X. Li, Xiang; H. Huang, Hao; Y.S. Liu, Yu Shen; Y. Fang, Yi
    Year: 2025
    Journal: IEEE Transactions on Multimedia
    Citations: 4

  • Title: Learning general features to bridge the cross-domain gaps in few-shot learning
    Authors: X. Li, Xiang; H. Luo, Hui; G. Zhou, Gaofan; M. Li, Meihui; Y. Liu, Yunfeng
    Year: 2024
    Journal: Knowledge-Based Systems
    Citations: 1

Mehrasa Ahmadipour | Information Theory | Best Researcher Award

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Dr. Mehrasa Ahmadipour | Information Theory | Best Researcher Award

Postdoc at UMPA, ens de lyon, France

Mehrasa Ahmadipour is a highly qualified candidate for the Best Researcher Award, with a Ph.D. in Information Theory from Institut Polytechnique de Paris and postdoctoral research at ENS Lyon in Sequential Statistics and Reinforcement Learning. Her expertise spans Multi-Armed Bandit Problems, ISAC, Neural Networks, and Physical Layer Security. She has contributed significantly as a guest editor, reviewer for IEEE journals, and session chair at IEEE ISIT 2023. With teaching experience in Information Theory, Cryptography, and Probability, she has also supervised master’s students. Additionally, she has held key roles in organizing academic conferences like CJC-MA 2024 and ISIT 2019. While her academic and research credentials are outstanding, strengthening her portfolio with more high-impact publications, citations, research funding, and industry collaborations would further enhance her profile. Overall, her research excellence, leadership, and contributions to the field make her a strong contender for the award.

Professional Profile 

Education🎓

Mehrasa Ahmadipour has a strong academic background in Electrical Engineering and Information Theory. She earned her Ph.D. from Institut Polytechnique de Paris (Télécom Paris) in 2022, specializing in Integrated Sensing and Communication (ISAC) under the supervision of Michele Wigger. Her doctoral research focused on an information-theoretic approach to ISAC, contributing to advancements in wireless communication and signal processing. Prior to that, she completed her M.Sc. in Electrical Engineering (Telecommunications Systems and Security) at the University of Tehran, where she worked on Physical Layer Authentication and Covert Communication in Wireless Networks. She earned her B.Sc. in Electrical Engineering from Iran University of Science and Technology (IUST), with a focus on Hyper Spectral Image Processing. Her academic journey began at the National Organization for Development of Exceptional Talents (NODET), where she specialized in Physics and Mathematics, ranking in the top 0.1% in university entrance exams, demonstrating exceptional academic excellence.

Professional Experience 📝

Mehrasa Ahmadipour has extensive professional experience in research and academia, focusing on Information Theory, Machine Learning, and Telecommunications. She is currently a Postdoctoral Researcher at École Normale Supérieure de Lyon, working on Sequential Statistics and Reinforcement Learning under the supervision of Aurélien Garivier. Her research explores advanced statistical methods and optimization techniques in decision-making processes. Previously, she completed a Master’s internship at Télécom ParisTech, where she applied information-theoretic tools to Machine Learning. Throughout her career, she has contributed to various research areas, including Multi-Armed Bandit Problems, Integrated Sensing and Communication (ISAC), Physical Layer Security, and Covert Communication. In addition to her research, she has played a key role in academia, serving as a session chair at IEEE ISIT 2023, a guest editor for Entropy, and a reviewer for IEEE journals and conferences. Her strong research background, leadership roles, and technical expertise position her as a leading scholar in her field.

Research Interest🔎

Mehrasa Ahmadipour’s research interests lie at the intersection of Information Theory, Machine Learning, and Wireless Communications, with a strong focus on Sequential Statistics and Reinforcement Learning. She is particularly interested in Multi-Armed Bandit Problems, exploring their applications in decision-making, resource allocation, and optimization. Her work in Integrated Sensing and Communication (ISAC) has contributed to advancements in wireless networks, particularly in Multiple Access and Broadcast Channels. She has also conducted research on Physical Layer Security, Covert Communication, and Neural Networks, applying information-theoretic tools to enhance security and efficiency in modern communication systems. Additionally, her research in Machine Learning interpretation using information theory has provided insights into neural network behavior. Through her multidisciplinary expertise, she aims to bridge the gap between statistical learning, security, and telecommunications, making significant contributions to next-generation communication systems and artificial intelligence applications.

Award and Honor🏆

Mehrasa Ahmadipour has received several prestigious awards and honors for her academic excellence and research achievements. She ranked in the top 0.1% of all participants in the university entrance exam (Concours) in 2010, demonstrating exceptional academic ability. Later, in 2016, she ranked in the top 1% of all participants in the university entrance exam for the master’s program, further solidifying her position as a top-tier student in Electrical Engineering. Her research contributions in Information Theory, Reinforcement Learning, and Wireless Communications have earned her recognition in the academic community, including invitations to serve as a guest editor for Entropy and as a session chair at IEEE ISIT 2023. Additionally, she has been actively involved in reviewing for leading IEEE journals and conferences, contributing to the advancement of knowledge in her field. Her outstanding academic record, research impact, and leadership roles highlight her as a distinguished scholar.

Research Skill🔬

Mehrasa Ahmadipour possesses a diverse set of research skills in Information Theory, Machine Learning, and Wireless Communications. She is highly proficient in Sequential Statistics, Reinforcement Learning, and Multi-Armed Bandit Problems, with expertise in designing and analyzing optimization algorithms for decision-making processes. Her work on Integrated Sensing and Communication (ISAC) demonstrates her ability to apply information-theoretic approaches to modern wireless networks, particularly in Multiple Access and Broadcast Channels. Additionally, she has strong skills in Physical Layer Security, Covert Communication, and Neural Network Interpretation, utilizing advanced mathematical modeling and probabilistic methods. She is also an experienced reviewer and editor for leading IEEE journals, demonstrating her ability to critically evaluate cutting-edge research. Her technical skills include proficiency in MATLAB, Simulink, Python, and C++, enabling her to implement and validate complex theoretical models. Her strong analytical thinking, problem-solving abilities, and interdisciplinary expertise make her a highly skilled researcher.

Conclusion💡

Mehrasa Ahmadipour is a highly qualified and competitive candidate for the Best Researcher Award, given her strong research background, postdoctoral contributions, peer-reviewing roles, and teaching experience. However, to strengthen the nomination, focusing on high-impact publications, citation impact, research funding, and industrial collaborations would further solidify her case. If her publication and citation metrics are strong, she would be an excellent choice for this award.

Publications Top Noted✍️

  • Title: An information-theoretic approach to joint sensing and communication
    Authors: M. Ahmadipour, M. Kobayashi, M. Wigger, G. Caire
    Year: 2022
    Citations: 109

  • Title: Joint sensing and communication over memoryless broadcast channels
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2021
    Citations: 32

  • Title: An information-theoretic approach to collaborative integrated sensing and communication for two-transmitter systems
    Authors: M. Ahmadipour, M. Wigger
    Year: 2023
    Citations: 18

  • Title: Strong converses for memoryless bi-static ISAC
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 13

  • Title: Coding for sensing: An improved scheme for integrated sensing and communication over MACs
    Authors: M. Ahmadipour, M. Wigger, M. Kobayashi
    Year: 2022
    Citations: 13

  • Title: Integrated communication and receiver sensing with security constraints on message and state
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2023
    Citations: 11

  • Title: Covert communication over a compound discrete memoryless channel
    Authors: M. Ahmadipour, S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan
    Year: 2019
    Citations: 10

  • Title: State masking over a two-state compound channel
    Authors: S. Salehkalaibar, M.H. Yassaee, V.Y.F. Tan, M. Ahmadipour
    Year: 2021
    Citations: 3

  • Title: Strong Converse for Bi-Static ISAC with Two Detection-Error Exponents
    Authors: M. Ahmadipour, M. Wigger, S. Shamai
    Year: 2024
    Citations: 2

Hafiz Khan | Machine Learning | Best Researcher Award

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Prof. Dr. Hafiz Khan | Machine Learning | Best Researcher Award

Professor at Texas Tech University Health Sciences Center, United States

Dr. Hafiz M. R. Khan is a Full Professor of Biostatistics at Texas Tech University Health Sciences Center, with an extensive academic and research background. He holds a Ph.D. in Statistics from the University of Western Ontario and has postdoctoral training in Bioinformatics. His career spans multiple institutions, including Florida International University and the University of Medicine & Dentistry of New Jersey. Dr. Khan has held leadership roles such as Associate Chair and Director of Outcome Measures, contributing significantly to academic committees and research initiatives. He has published extensively in peer-reviewed journals, focusing on biostatistics, public health, and cognitive impairment research. His strengths for the Best Researcher Award include a strong publication record, leadership in academia, and interdisciplinary collaboration. Areas for improvement may include further engagement in international research projects. Overall, his contributions to biostatistics and public health research make him a strong candidate for the Best Researcher Award.

Professional Profile 

Education

Dr. Hafiz M. R. Khan has a strong educational background in statistics and biostatistics. He earned his Ph.D. in Statistics from the University of Western Ontario, Canada, where he specialized in statistical methodologies and their applications in health sciences. To further enhance his expertise, he completed postdoctoral training in Bioinformatics, gaining advanced knowledge in computational biology and data analysis. His academic journey also includes a Master’s and Bachelor’s degree in Statistics, which provided him with a solid foundation in quantitative analysis and research methods. Throughout his education, Dr. Khan focused on interdisciplinary applications of statistics, particularly in public health, epidemiology, and biomedical sciences. His strong academic credentials have enabled him to contribute significantly to research, teaching, and mentoring students in biostatistics and public health. His education has played a pivotal role in shaping his career, allowing him to bridge the gap between statistical theory and real-world health applications.

Professional Experience

Dr. Hafiz M. R. Khan has an extensive professional background in statistics, biostatistics, and public health research. He has held various academic and research positions, contributing significantly to statistical methodologies in biomedical and epidemiological studies. As a professor and researcher, he has taught biostatistics, data analysis, and public health courses at reputable institutions, mentoring numerous students and professionals. His expertise extends to consulting for healthcare organizations and research institutions, where he applies statistical models to solve complex health-related problems. Dr. Khan has also collaborated on interdisciplinary projects involving bioinformatics, machine learning, and predictive analytics in healthcare. His professional journey includes publishing high-impact research papers, serving as a peer reviewer for scientific journals, and participating in international conferences. His work has been instrumental in advancing statistical applications in medicine and public health, bridging the gap between theoretical research and practical implementation in real-world health challenges.

Research Interest

Dr. Hafiz M. R. Khan’s research interests lie at the intersection of biostatistics, epidemiology, and public health, with a strong focus on statistical modeling, predictive analytics, and machine learning applications in healthcare. He is particularly interested in developing advanced statistical methodologies to analyze complex biomedical data, improve disease prediction models, and enhance public health decision-making. His work explores the integration of statistical techniques with bioinformatics to study genetic influences on diseases and health outcomes. Additionally, he investigates the application of artificial intelligence in medical research, aiming to optimize diagnostic accuracy and treatment effectiveness. Dr. Khan is also passionate about global health issues, including infectious disease surveillance, health disparities, and aging populations. Through interdisciplinary collaborations, he strives to bridge the gap between statistical theory and real-world healthcare applications, contributing to innovative solutions that enhance patient care, policy-making, and public health interventions worldwide.

Award and Honor

Dr. Hafiz M. R. Khan has received numerous awards and honors in recognition of his outstanding contributions to biostatistics, public health, and epidemiology. He has been honored with prestigious research grants and fellowships from esteemed institutions, highlighting his excellence in statistical modeling and healthcare analytics. His groundbreaking work has earned him accolades such as the Best Researcher Award and Excellence in Public Health Research recognition. Dr. Khan has been invited as a keynote speaker at international conferences and has received distinguished scholar awards for his impactful publications. His dedication to academic excellence has also been acknowledged through teaching awards, mentoring recognitions, and leadership roles in professional organizations. Additionally, he has been recognized for his contributions to global health initiatives, demonstrating his commitment to improving healthcare outcomes. These awards and honors underscore his influence in the field and his continuous efforts to advance research, education, and policy in health sciences.

Research Skill

Dr. Hafiz M. R. Khan possesses exceptional research skills in biostatistics, public health, and epidemiology, enabling him to conduct advanced statistical analyses and develop innovative models for healthcare studies. His expertise includes data analysis, predictive modeling, machine learning applications in health research, and designing population-based studies. He has a strong command of statistical software such as R, SPSS, SAS, and STATA, which he utilizes to interpret complex datasets effectively. Dr. Khan excels in systematic reviews, meta-analysis, and quantitative research methodologies, ensuring rigorous scientific inquiry and evidence-based conclusions. His ability to synthesize large datasets and extract meaningful insights has contributed significantly to policy recommendations and healthcare improvements. Additionally, his collaborative approach to interdisciplinary research allows him to work seamlessly with experts from diverse fields. His critical thinking, problem-solving abilities, and meticulous research design skills make him a valuable contributor to advancing public health, epidemiology, and statistical sciences.

Conclusion

Dr. Hafiz M. R. Khan is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to academia, research, and public health. His leadership roles, mentoring, and commitment to advancing Biostatistics make him a strong contender. However, enhancing visibility of research impact, citations, international collaborations, and applied innovations could further strengthen his application.

Publications Top Noted

  • Title: Metabolic syndrome in aboriginal Canadians: prevalence and genetic associations
    Authors: RL Pollex, AJG Hanley, B Zinman, SB Harris, HMR Khan, RA Hegele
    Year: 2006
    Citations: 145

  • Title: Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions
    Authors: K Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, …
    Year: 2005
    Citations: 142

  • Title: Genetic Variation in PPARG Encoding Peroxisome Proliferator-Activated Receptor γ Associated With Carotid Atherosclerosis
    Authors: KZ Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, B Zinman, …
    Year: 2004
    Citations: 123

  • Title: Guillain–Barré syndrome after Gardasil vaccination: data from vaccine adverse event reporting system 2006–2009
    Authors: N Souayah, PA Michas-Martin, A Nasar, N Krivitskaya, HA Yacoub, …
    Year: 2011
    Citations: 120

  • Title: Type 2 diabetes and its correlates among adults in Bangladesh: a population-based study
    Authors: MAB Chowdhury, MJ Uddin, HMR Khan, MR Haque
    Year: 2015
    Citations: 110

  • Title: Physical therapists’ attitudes, knowledge, and practice approaches regarding people who are obese
    Authors: S Sack, DR Radler, KK Mairella, R Touger-Decker, H Khan
    Year: 2009
    Citations: 78

  • Title: Trends in outcomes and hospitalization costs for traumatic brain injury in adult patients in the United States
    Authors: K Farhad, HMR Khan, AB Ji, HA Yacoub, AI Qureshi, N Souayah
    Year: 2013
    Citations: 56

  • Title: Predictive inference from a two-parameter Rayleigh life model given a doubly censored sample
    Authors: HMR Khan, SB Provost, A Singh
    Year: 2010
    Citations: 49

  • Title: Optimizing RNA extraction yield from whole blood for microarray gene expression analysis
    Authors: J Wang, JF Robinson, HMR Khan, DE Carter, J McKinney, BA Miskie, …
    Year: 2004
    Citations: 48

  • Title: Secondhand smoke exposure reduction intervention in Chinese households of young children: a randomized controlled trial
    Authors: AS Abdullah, F Hua, H Khan, X Xia, Q Bing, K Tarang, JP Winickoff
    Year: 2015
    Citations: 45

  • Title: Statistical machine learning approaches to liver disease prediction
    Authors: F Mostafa, E Hasan, M Williamson, H Khan
    Year: 2021
    Citations: 40

  • Title: The safety profile of home infusion of intravenous immunoglobulin in patients with neuroimmunologic disorders
    Authors: N Souayah, A Hasan, HMR Khan, HA Yacoub, M Jafri
    Year: 2011
    Citations: 34

  • Title: Tumor-infiltrating lymphocytes (TILs) as a biomarker of abscopal effect of cryoablation in breast cancer: A pilot study
    Authors: SY Khan, MW Melkus, F Rasha, M Castro, V Chu, L Brandi, H Khan, …
    Year: 2022
    Citations: 31

  • Title: Vulnerability prioritization, root cause analysis, and mitigation of secure data analytic framework implemented with MongoDB on Singularity Linux containers
    Authors: AM Dissanayaka, S Mengel, L Gittner, H Khan
    Year: 2020
    Citations: 31

  • Title: Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening?
    Authors: A Perisetti, H Khan, NE George, R Yendala, A Rafiq, S Blakely, …
    Year: 2018
    Citations: 31